Q12 I ran

df.corr()

and received an error (assuming I am in pandas 2).

What's the most likely cause?

Question

Q12 I ran

df.corr()

and received an error (assuming I am in pandas 2).

What's the most likely cause?

BlackTom AI · Accepted Answer

Reasserting the scenario: the user ran df.corr() in pandas 2 and an error occurred. We will evaluate each potential cause to understand why the error happened.
Option 1: The DataFrame has missing values. While missing values can affect certain computations, corr() typically handles missing data by pairwise deletion or other internal strategies, and missing values alone are unlikely to cause a hard error just from calling corr(). In many cases, missing values are simply ignored or result in NaN in the correlation results, not a crash or error message. Therefore this reason is not the most likely cause for an error thrown by corr().
Option 2: The DataFrame has string/object columns and corr() tried to include them as the default is numeric_only = False. This statement points to a structural issue: corr() operates on numeric data. If the DataFrame contains non-numeric (string/object) columns and the function configuration attempts to include them, it can raise an error because correlation is defined for numeric data only. The note about numeric_only being False suggests that non-numeric columns were not automatically excluded, leading to the attempt to compute correlations with incompatible column types. This aligns with a plausible, direct cause of an error in this context.
Option 3: The DataFrame has duplicated column names. Duplicate column names can cause ambiguity in operations, but corr() usually aligns rows and columns by position or label, and pandas has mechanisms to handle duplicates in many cases. While duplicates could complicate some operations, they are a less common direct trigger for an error specifically in corr() unless there are further underlying type or alignment issues. Hence this is a less likely primary cause.
Option 4: A column has non-scalar objects (list/dictionary). If a column contains non-scalar objects, corr() may fail due to attempting numeric computations on non-numeric, non-scalar data. This is a plausible cause for an error when numeric_only handling is not robust enough to exclude such columns. However, in practice this situation would usually correspond to non-numeric data types rather than non-scalar content per se, and depending on how the function handles object dtype, it could still trigger an error. It remains a possible cause, but the likelihood depends on how non-numeric/object data are represented in the DataFrame and how strictly corr() enforces numeric data.
In summary, while several issues could lead to an error when calling corr(), the specific message and the given answer point toward the root cause being the presence of string/object columns and corr() attempting to include them due to the numeric_only setting. This matches Option 2 as the most direct explanation in this scenario. The other options describe plausible data problems but are not as tightly aligned with a typical corr() error under the stated assumptions.

ISBA2401 - Data Analytics with Python (MW 05:45 PM, MW 7:35 PM, TTh 7:35PM) Midterm Multiple Choice (FA2025 - MW735) - Requires Respondus LockDown Browser

Q12 I ran df.corr() and received an error (assuming I am in pandas 2). What's the most likely cause?

查看解析

登录即可查看完整答案

类似问题

In pandas, which function is used to calculate the correlation between columns in DataFrame df?

In a consumer society, many adults channel creativity into buying things

Economic stress and unpredictable times have resulted in a booming industry for self-help products

People born without creativity never can develop it

A product has a selling price of $20, a contribution margin ratio of 40% and fixed cost of $120,000. To make a profit of $30,000. The number of units that must be sold is: Type the number without $ and a comma. Eg: 20000

Which of the following statement regarding cost is correct:

Under the assumptions used in cost-volume-profit analysis, as the activity increases:

The CVP and break-even analysis are supported by a range of important assumptions. Which of the following is not an assumption of the CVP:

更多留学生实用工具

考试浏览器助手

风格化写作助手

论文查重助手

文献引用助手

课堂转译助手

课堂笔记助手

Quiz搜索助手

学校历年真题

智能刷题助手

智能匹配练习

希望你的学习变得更简单